Reconciling Folksonomic Tagging with Taxa for Bioacoustic Annotations

Truskinger, Anthony; Newmarch, Ian; Cottman-Fields, Mark; Wimmer, Jason; Towsey, Michael; Zhang, Jinglan; Roe, Paul

doi:10.1007/978-3-642-41230-1_25

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…The tags are descriptive and free form, however, as is the case with most systems that have human components, errors are generated. We have done work to automatically correct, clean, and link our folksonomic tags to taxonomic data structures [6]. Ensuring the tags on our annotations stay clean ensure our core data output for ecologists is not affected by human errors.…”

Section: Semi-automated Approachmentioning

confidence: 98%

Managing and Analysing Big Audio Data for Environmental Monitoring

Zhang

Huang

Cottman-Fields

et al. 2013

2013 IEEE 16th International Conference on Computational Science and Engineering

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Environmental monitoring is becoming critical as human activity and climate change place greater pressures on biodiversity, leading to an increasing need for data to make informed decisions. Acoustic sensors can help collect data across large areas for extended periods making them attractive in environmental monitoring.However, managing and analysing large volumes of environmental acoustic data is a great challenge and is consequently hindering the effective utilization of the big dataset collected. This paper presents an overview of our current techniques for collecting, storing and analysing large volumes of acoustic data efficiently, accurately, and cost-effectively.

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Section: Semi-automated Approachmentioning

confidence: 98%

Managing and Analysing Big Audio Data for Environmental Monitoring

Zhang

Huang

Cottman-Fields

et al. 2013

2013 IEEE 16th International Conference on Computational Science and Engineering

Self Cite

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“…The tag labels are used as the output of the decision support tool. The tags are textual and suffer from numerous spelling and grammatical errors, suffixes that conflate their semantics, and various forms of synonymy (Truskinger et al, 2013). The tag 'unknown' allowed an additional class to accommodate analyst uncertainty.…”

Section: Featuresmentioning

confidence: 99%

Decision support for the efficient annotation of bioacoustic events

Truskinger¹,

Towsey²,

Roe³

2015

Ecological Informatics

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Acoustic sensors allow scientists to scale environmental monitoring over large spatiotemporal scales. The faunal vocalisations captured by these sensors can answer ecological questions, however, identifying these vocalisations within recorded audio is difficult: automatic recognition is currently intractable and manual recognition is slow and error prone. In this paper, a semi-automated approach to call recognition is presented. An automated decision support tool is tested that assists users in the manual annotation process. The respective strengths of human and computer analysis are used to complement one another. The tool recommends the species of an unknown vocalisation and thereby minimises the need for the memorization of a large corpus of vocalisations. In the case of a folksonomic tagging system, recommending species tags also minimises the proliferation of redundant tag categories. We describe two algorithms: (1) a "naïve" decision support tool (16%-64% sensitivity) with efficiency of O(n) but which becomes unscalable as more data is added and (2) a scalable alternative with 48% sensitivity and an efficiency ofO(log n). The improved algorithm was also tested in a HTML-based annotation prototype. The result of this work is a decision support tool for annotating faunal acoustic events that may be utilised by other bioacoustics projects.

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